ATM (asynchronous transfer mode) is an essential technology for integrating multimedia services in high-speed networks. Because of bursty traffic characteristics and various quality of service (QoS) as well as bandwidth requirements for these multimedia services, an ATM network must have an appropriate connection admission control (CAC) scheme to guarantee the QoS for existing calls as well as to achieve high system utilization.
Conventional CAC schemes that utilize either capacity estimation or buffer thresholds suffer from some fundamental limitations. One of the limitations is the difficulty for a network to acquire complete statistics of input traffic. As a result, it is not easy to accurately determine the effective thresholds or equivalent capacity in various bursty traffic flow conditions of ATM networks. Also, these conventional schemes have optimal solution only under steady state. A control scheme which dynamically regulate traffic flows according to changing network conditions, however, requires understanding of its dynamics. The rationale and principles underlying the nature and choice of thresholds or equivalent capacity under dynamic conditions are unclear. Networks are forced to make a decision based on the incomplete information and therefore the decision process is full of uncertainty. Thus, due to the unpredictable statistical fluctuations of the system, decision error will always be accompanied with these control schemes and result in performance degradation.
Recently, fuzzy logic systems have been widely applied to deal with the CAC related problems in ATM network (R. G. Cheng and C. J. Chang, "Design of a fuzzy traffic controller for ATM networks," IEEE/ACM Trans. Networking, vol. 4, no. 3, pp. 460-469, June 1996). The results reveal that fuzzy set theory could provide a robust mathematical framework for dealing with "real-world" imprecision and the fuzzy approach exhibits a soft behavior which means to have a greater ability to adapt to dynamic, imprecise, and bursty environment (R. G. Cheng and C. J. Chang, "Design of a fuzzy traffic controller for ATM networks," IEEE/ACM Trans. Networking, vol. 4, no. 3, pp. 460-469, June 1996). In (R. G. Cheng and C. J. Chang, "Design of a fuzzy traffic controller for ATM networks," IEEE/ACM Trans. Networking, vol. 4, no. 3, pp. 460-469, June 1996), a fuzzy traffic controller which simultaneously incorporates CAC and congestion control was proposed. Comparative studies show that the proposed fuzzy approaches significantly improve the system performance over the conventional approaches. However, it is found that there is still no clear and general technique presented to map the existing knowledge on traffic control to those design parameters of a fuzzy logic controller. In order to ease the design procedure and to have a better control result, self-learning capability should be deployed in the fuzzy logic controller.
Self-learning capability of the neural network has been applied to characterize the relationship between the input traffic and the system performance. Simulation results showed that neural networks have several valuable properties (i.e., adaptive learning, high computation rates due to the massive parallelism of the hardware implementation, generalization from learning, and high degree of fault tolerance due to distributed processing) for implementing ATM traffic control. Actually, the conventional CAC, fuzzy-logic-based CAC, and neural-net-based CAC schemes have their benefits in dealing with CAC. The conventional CAC, based on a mathematical analysis, provides robust solutions for different kinds of traffic environment but suffers from drawbacks of estimation error (due to modeling) and approximation error (due to the requisite calculations in real time) and is not suitable for dynamic environments. The fuzzy-logic-based CAC is excellent in dealing with real-world imprecision and has a greater ability to adapt to dynamic, imprecise, and bursty environment, but lacks the learning capability to automatically construct its rule structure and membership functions to achieve optimal performance. The neural-net approach provides the learning and adoption capability which could reduce the estimation error of conventional CAC and achieve optimal performance of fuzzy logic controller, however, the knowledge of conventional methods may not be easily adopted in the design of neural networks.
U.S. Pat. No. 5,179,556 proposed a traffic control mechanism directed to prioritizing cells for selected transmission depending on whether or not the cells are part of a burst. A source of cells "encodes" the cells to indicate whether the cells are at the start, middle or end of a train of cells in a burst. Lone cells not part of a burst are not encoded. Each node has a state machine associated with each outgoing link. The state machine determines whether or not to block or transmit received cells. (The state machine can only have two states: block or transmit). The state machine decodes each encoded cell and used the burst codes as a basis for permitting a transition from one state to another. That is, a state machine can transition from the blocking state to the transmit state, or vice versa, only at the beginning or end of a burst, not in the middle of a burst. This patent also proposed a connection admission control mechanism which uses a single threshold to determine whether or not to admit a new communication to a node. The problem with this patent is that traffic load are difficult to predict thus making it difficult to establish appropriate criterion (i.e. thresholds) for determining when to transition from a transmit state to a blocking state or when there is available capacity to support new communication at a node.
U.S. Pat. No. 5,341,366 proposed to use fuzzy logic and fuzzy set theory in congestion control and admission control. Fuzzy logic and fuzzy sets can best be explained in comparison to ordinary logic and sets. In ordinary logic, each constant and logical predicate may have only one of the two values, namely, true or false (or 0 or 1). In logic, each constant and predicate may have a whole continuum of values between true and false or 0 and 1. These values represent a possibility between 0 and 1 that the corresponding fuzzy logic constant or predicate is true. Likewise, in ordinary set theory, a predicate function can be defined over a group of set elements which determines whether or not a set element is contained in a given set with absolute certainty. A mathematical expression which converts values to such possibilities is referred to as a membership function. Membership functions can be continuous or can be discrete with plural quantum levels. U.S. Pat. No.5,341,366 uses fuzzy logic to control the admission of new communications at a node. However, there does not exist a general and clear technique to design the fuzzy logic controller. Moreover, the controller is hard to adjust itself to adapt to dynamic environment.